| Recent advances and applications of deep learning methods in materials science K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... npj Computational Materials 8 (1), 59, 2022 | 585 | 2022 |
| Overview: Computer vision and machine learning for microstructural characterization and analysis EA Holm, R Cohn, N Gao, AR Kitahara, TP Matson, B Lei, SR Yarasi Metallurgical and Materials Transactions A 51, 5985-5999, 2020 | 214 | 2020 |
| Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data R Cohn, E Holm Integrating Materials and Manufacturing Innovation 10 (2), 231-244, 2021 | 96 | 2021 |
| Recent advances and applications of deep learning methods in materials science. npj Computational Materials, 8 (1): 59 K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... URL: https://doi. org/10.1038/s41524-022-00734-6, doi 10, 2022 | 53 | 2022 |
| Instance segmentation for direct measurements of satellites in metal powders and automated microstructural characterization from image data R Cohn, I Anderson, T Prost, J Tiarks, E White, E Holm Jom 73 (7), 2159-2172, 2021 | 37 | 2021 |
| Recent advances and applications of deep learning methods in materials science. npj Comput Mater 8 K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... Doi, 2022 | 31 | 2022 |
| Extreme abnormal grain growth: connecting mechanisms to microstructural outcomes CE Krill III, EA Holm, JM Dake, R Cohn, K Holíková, F Andorfer Annual Review of Materials Research 53 (1), 319-345, 2023 | 14 | 2023 |
| Recent advances and applications of deep learning methods in materials science. npj Comput Mater 2022; 8: 59 K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... DOI, 0 | 7 | |
| Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data. Integrating Materials and Manufacturing Innovation, 10 (2), 231–244 R Cohn, E Holm | 6 | 2021 |
| Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution R Cohn, E Holm arXiv preprint arXiv:2110.09326, 2021 | 3 | 2021 |
| Computer vision and machine learning to quantify microstructure EA Holm, R Cohn, N Gao, AR Kitahara, B Lei, SR Yarasi, TP Matson AM&P Technical Articles 179 (2), 13-18, 2021 | 2 | 2021 |
| Calorimetric study with uncertainty analysis to investigate the precipitation kinetics in a nanostructured Al composite R Cohn, B Fullenwider, K Ma, JM Schoenung Advanced Engineering Materials 20 (4), 1700728, 2018 | 2 | 2018 |
| Computer vision and deep learning for microstructural modeling and automated characterization of materials RC Cohn Carnegie Mellon University, 2022 | 1 | 2022 |
| Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution R Cohn, EA Holm Scientific Reports 14 (1), 1-11, 2024 | | 2024 |
| Instance Segmentation for Occluded Particles K Farmer, R Cohn, E Holm Kansas City Nuclear Security Campus (KCNSC), Kansas City, MO (United States), 2023 | | 2023 |
